The advent of the Internet of Things (IoT) has brought significant benefits to various industries, but at the same time, it has also led to an increase in cyber threats. Therefore, Intrusion Detection Systems (IDS) play a crucial role in ensuring the security of IoT devices. One of the challenges faced by IDS is the limited memory available in IoT devices. This makes it necessary to optimize memory usage for efficient intrusion detection.
In this context, P. Suresh's research on "Efficient Memory Optimization for IoT Intrusion Detection" is an essential contribution to IoT security. The study focuses on improving the performance of IDS by optimizing memory usage. The research proposes innovative techniques for efficient memory allocation, management, and access in IoT devices.
The proposed solution employs machine learning, deep learning, and artificial intelligence techniques, along with big data analytics and data mining, for anomaly detection, pattern recognition, and threat detection. The IDS also includes real-time monitoring, data processing, and data storage, retrieval, and analysis capabilities.
The research evaluates the performance of the proposed IDS by conducting experimental studies and benchmarking against existing systems. The results show that the proposed solution achieves better intrusion detection rates with reduced memory usage, improved system scalability, and enhanced energy efficiency.
The study's findings provide valuable insights into memory optimization techniques for IoT intrusion detection, highlighting the need for efficient resource utilization and system performance. The research also emphasizes the significance of system design, architecture, integration, and testing in ensuring reliable and secure IoT devices.
